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Hits 41 – 60 of 1.029

41
KOAS: Korean Text Offensiveness Analysis System ...
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42
Contrastive Code Representation Learning ...
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43
Does Putting a Linguist in the Loop Improve NLU Data Collection ...
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44
What are we learning from language? ...
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45
Machine Translation Decoding beyond Beam Search ...
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46
Say `YES' to Positivity: Detecting Toxic Language in Workplace Communications ...
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47
Unsupervised Multi-View Post-OCR Error Correction With Language Models ...
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48
AttentionRank: Unsupervised Keyphrase Extraction using Self and Cross Attentions ...
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49
ProtoInfoMax: Prototypical Networks with Mutual Information Maximization for Out-of-Domain Detection ...
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50
Multi-granularity Textual Adversarial Attack with Behavior Cloning ...
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51
Automatic Fact-Checking with Document-level Annotations using BERT and Multiple Instance Learning ...
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52
Towards the Early Detection of Child Predators in Chat Rooms: A BERT-based Approach ...
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53
TSDAE: Using Transformer-based Sequential Denoising Auto-Encoder for Unsupervised Sentence Embedding Learning ...
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54
WebSRC: A Dataset for Web-Based Structural Reading Comprehension ...
Abstract: Anthology paper link: https://aclanthology.org/2021.emnlp-main.343/ Abstract: Web search is an essential way for humans to obtain information, but it’s still a great challenge for machines to understand the contents of web pages. In this paper, we introduce the task of structural reading comprehension (SRC) on the web. Given a web page and a question about it, the task is to find the answer from the web page. This task requires a system not only to understand the semantics of texts but also the structure of the web page. Moreover, we proposed WebSRC, a novel Web-based Structural Reading Comprehension dataset. WebSRC consists of 400K question-answer pairs, which are collected from 6.4K web pages. Along with the QA pairs, corresponding HTML source code, screenshots, and metadata are also provided in our dataset. Each question in WebSRC requires a certain structural understanding of a web page to answer, and the answer is either a text span on the web page or yes/no. We evaluate various baselines on our dataset ...
Keyword: Computational Linguistics; Machine Learning; Machine Learning and Data Mining; Natural Language Processing; Question-Answering Systems
URL: https://dx.doi.org/10.48448/ype8-7m65
https://underline.io/lecture/38027-websrc-a-dataset-for-web-based-structural-reading-comprehension
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55
Improving Math Word Problems with Pre-trained Knowledge and Hierarchical Reasoning ...
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56
Semantic Categorization of Social Knowledge for Commonsense Question Answering ...
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57
Adversarial Examples for Evaluating Math Word Problem Solvers ...
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58
Pre-train or Annotate? Domain Adaptation with a Constrained Budget ...
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59
Corpus-based Open-Domain Event Type Induction ...
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60
Learning with Different Amounts of Annotation: From Zero to Many Labels ...
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